Average Total Fertility Rate by Geographic Region (1960-2021)
Bradley Cardona
2023-06-28
Case Study Outline
- Case Study Title: Average Total Fertility Rate by Geographic Region (1960-2021)
- Case Study Question: What was the Average Total Fertility Rate by Geographic Region for the last 50 years?
- Dataset: https://data.worldbank.org/indicator/SP.DYN.TFRT.IN?end=2021&start=2021&view=map&year=2021
Install required packages
library(tidyverse) # helps wrangle data## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(lubridate) # helps wrangle date attributes
library(ggplot2) # helps visualize data
library(readxl)
getwd() # displays working directory## [1] "/Users/bradleycardona/Documents/data_analytics/fertility_rate_case_study"# set working directory to simplify calls to data
setwd("~/Documents/data_analytics/fertility_rate_case_study/") Step 1: Collect data
fertility_rates <- read_xls("fertility_rates.xls")
head(fertility_rates)## # A tibble: 6 × 67
## `Country Name` `Country Code` `Indicator Name` `Indicator Code` `1960` `1961`
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Aruba ABW Fertility rate,… SP.DYN.TFRT.IN 4.82 4.66
## 2 Africa Eastern… AFE Fertility rate,… SP.DYN.TFRT.IN 6.72 6.74
## 3 Afghanistan AFG Fertility rate,… SP.DYN.TFRT.IN 7.28 7.28
## 4 Africa Western… AFW Fertility rate,… SP.DYN.TFRT.IN 6.46 6.47
## 5 Angola AGO Fertility rate,… SP.DYN.TFRT.IN 6.71 6.79
## 6 Albania ALB Fertility rate,… SP.DYN.TFRT.IN 6.46 6.35
## # ℹ 61 more variables: `1962` <dbl>, `1963` <dbl>, `1964` <dbl>, `1965` <dbl>,
## # `1966` <dbl>, `1967` <dbl>, `1968` <dbl>, `1969` <dbl>, `1970` <dbl>,
## # `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>, `1975` <dbl>,
## # `1976` <dbl>, `1977` <dbl>, `1978` <dbl>, `1979` <dbl>, `1980` <dbl>,
## # `1981` <dbl>, `1982` <dbl>, `1983` <dbl>, `1984` <dbl>, `1985` <dbl>,
## # `1986` <dbl>, `1987` <dbl>, `1988` <dbl>, `1989` <dbl>, `1990` <dbl>,
## # `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>, `1995` <dbl>, …Step 2: Wrangle data, look for any incongruencies
colnames(fertility_rates) # Column names## [1] "Country Name" "Country Code" "Indicator Name" "Indicator Code"
## [5] "1960" "1961" "1962" "1963"
## [9] "1964" "1965" "1966" "1967"
## [13] "1968" "1969" "1970" "1971"
## [17] "1972" "1973" "1974" "1975"
## [21] "1976" "1977" "1978" "1979"
## [25] "1980" "1981" "1982" "1983"
## [29] "1984" "1985" "1986" "1987"
## [33] "1988" "1989" "1990" "1991"
## [37] "1992" "1993" "1994" "1995"
## [41] "1996" "1997" "1998" "1999"
## [45] "2000" "2001" "2002" "2003"
## [49] "2004" "2005" "2006" "2007"
## [53] "2008" "2009" "2010" "2011"
## [57] "2012" "2013" "2014" "2015"
## [61] "2016" "2017" "2018" "2019"
## [65] "2020" "2021" "2022"nrow(fertility_rates) # Number of rows## [1] 266dim(fertility_rates) # Dimensionss## [1] 266 67head(fertility_rates) # First 6 rows of data frame## # A tibble: 6 × 67
## `Country Name` `Country Code` `Indicator Name` `Indicator Code` `1960` `1961`
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Aruba ABW Fertility rate,… SP.DYN.TFRT.IN 4.82 4.66
## 2 Africa Eastern… AFE Fertility rate,… SP.DYN.TFRT.IN 6.72 6.74
## 3 Afghanistan AFG Fertility rate,… SP.DYN.TFRT.IN 7.28 7.28
## 4 Africa Western… AFW Fertility rate,… SP.DYN.TFRT.IN 6.46 6.47
## 5 Angola AGO Fertility rate,… SP.DYN.TFRT.IN 6.71 6.79
## 6 Albania ALB Fertility rate,… SP.DYN.TFRT.IN 6.46 6.35
## # ℹ 61 more variables: `1962` <dbl>, `1963` <dbl>, `1964` <dbl>, `1965` <dbl>,
## # `1966` <dbl>, `1967` <dbl>, `1968` <dbl>, `1969` <dbl>, `1970` <dbl>,
## # `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>, `1975` <dbl>,
## # `1976` <dbl>, `1977` <dbl>, `1978` <dbl>, `1979` <dbl>, `1980` <dbl>,
## # `1981` <dbl>, `1982` <dbl>, `1983` <dbl>, `1984` <dbl>, `1985` <dbl>,
## # `1986` <dbl>, `1987` <dbl>, `1988` <dbl>, `1989` <dbl>, `1990` <dbl>,
## # `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>, `1995` <dbl>, …str(fertility_rates) # Columns and respective data types (numeric, character, etc)## tibble [266 × 67] (S3: tbl_df/tbl/data.frame)
## $ Country Name : chr [1:266] "Aruba" "Africa Eastern and Southern" "Afghanistan" "Africa Western and Central" ...
## $ Country Code : chr [1:266] "ABW" "AFE" "AFG" "AFW" ...
## $ Indicator Name: chr [1:266] "Fertility rate, total (births per woman)" "Fertility rate, total (births per woman)" "Fertility rate, total (births per woman)" "Fertility rate, total (births per woman)" ...
## $ Indicator Code: chr [1:266] "SP.DYN.TFRT.IN" "SP.DYN.TFRT.IN" "SP.DYN.TFRT.IN" "SP.DYN.TFRT.IN" ...
## $ 1960 : num [1:266] 4.82 6.72 7.28 6.46 6.71 ...
## $ 1961 : num [1:266] 4.66 6.74 7.28 6.47 6.79 ...
## $ 1962 : num [1:266] 4.47 6.76 7.29 6.49 6.87 ...
## $ 1963 : num [1:266] 4.27 6.78 7.3 6.51 6.95 ...
## $ 1964 : num [1:266] 4.06 6.79 7.3 6.53 7.04 ...
## $ 1965 : num [1:266] 3.84 6.8 7.3 6.54 7.12 ...
## $ 1966 : num [1:266] 3.62 6.81 7.32 6.56 7.19 ...
## $ 1967 : num [1:266] 3.42 6.82 7.34 6.59 7.27 ...
## $ 1968 : num [1:266] 3.23 6.83 7.36 6.61 7.33 ...
## $ 1969 : num [1:266] 3.05 6.83 7.39 6.64 7.39 ...
## $ 1970 : num [1:266] 2.91 6.84 7.4 6.66 7.43 ...
## $ 1971 : num [1:266] 2.79 6.84 7.43 6.7 7.47 ...
## $ 1972 : num [1:266] 2.69 6.84 7.45 6.73 7.49 ...
## $ 1973 : num [1:266] 2.61 6.83 7.49 6.76 7.5 ...
## $ 1974 : num [1:266] 2.55 6.82 7.53 6.8 7.5 ...
## $ 1975 : num [1:266] 2.51 6.81 7.54 6.84 7.49 ...
## $ 1976 : num [1:266] 2.47 6.79 7.56 6.86 7.49 ...
## $ 1977 : num [1:266] 2.45 6.77 7.59 6.9 7.47 ...
## $ 1978 : num [1:266] 2.42 6.75 7.6 6.92 7.47 ...
## $ 1979 : num [1:266] 2.41 6.73 7.61 6.91 7.46 ...
## $ 1980 : num [1:266] 2.39 6.7 7.59 6.9 7.46 ...
## $ 1981 : num [1:266] 2.38 6.67 7.57 6.88 7.46 ...
## $ 1982 : num [1:266] 2.36 6.64 7.55 6.86 7.46 ...
## $ 1983 : num [1:266] 2.35 6.6 7.54 6.83 7.46 ...
## $ 1984 : num [1:266] 2.34 6.57 7.51 6.78 7.46 ...
## $ 1985 : num [1:266] 2.33 6.51 7.52 6.73 7.45 ...
## $ 1986 : num [1:266] 2.32 6.46 7.52 6.68 7.43 ...
## $ 1987 : num [1:266] 2.31 6.42 7.53 6.64 7.41 ...
## $ 1988 : num [1:266] 2.29 6.34 7.53 6.6 7.37 ...
## $ 1989 : num [1:266] 2.27 6.26 7.53 6.57 7.33 ...
## $ 1990 : num [1:266] 2.3 6.17 7.57 6.52 7.27 ...
## $ 1991 : num [1:266] 2.31 6.1 7.61 6.47 7.21 ...
## $ 1992 : num [1:266] 2.28 6.03 7.67 6.42 7.14 ...
## $ 1993 : num [1:266] 2.23 5.96 7.72 6.36 7.07 ...
## $ 1994 : num [1:266] 2.12 5.9 7.72 6.3 6.99 ...
## $ 1995 : num [1:266] 2.19 5.84 7.71 6.24 6.92 ...
## $ 1996 : num [1:266] 2.15 5.77 7.71 6.17 6.85 ...
## $ 1997 : num [1:266] 2.14 5.7 7.67 6.1 6.79 ...
## $ 1998 : num [1:266] 1.96 5.64 7.64 6.04 6.73 ...
## $ 1999 : num [1:266] 1.87 5.59 7.6 6.03 6.68 ...
## $ 2000 : num [1:266] 1.9 5.52 7.53 6.02 6.64 ...
## $ 2001 : num [1:266] 1.83 5.48 7.45 6 6.6 ...
## $ 2002 : num [1:266] 1.76 5.43 7.34 5.97 6.57 ...
## $ 2003 : num [1:266] 1.75 5.38 7.22 5.93 6.53 ...
## $ 2004 : num [1:266] 1.68 5.34 7.07 5.89 6.5 ...
## $ 2005 : num [1:266] 1.78 5.31 6.91 5.86 6.46 ...
## $ 2006 : num [1:266] 1.91 5.27 6.72 5.85 6.42 ...
## $ 2007 : num [1:266] 1.93 5.22 6.53 5.82 6.37 ...
## $ 2008 : num [1:266] 1.94 5.19 6.38 5.79 6.32 ...
## $ 2009 : num [1:266] 1.92 5.12 6.24 5.75 6.26 ...
## $ 2010 : num [1:266] 1.94 5.04 6.1 5.7 6.19 ...
## $ 2011 : num [1:266] 1.96 4.96 5.96 5.65 6.12 ...
## $ 2012 : num [1:266] 2.03 4.88 5.83 5.58 6.04 ...
## $ 2013 : num [1:266] 2.12 4.81 5.7 5.51 5.95 ...
## $ 2014 : num [1:266] 2.15 4.74 5.56 5.44 5.86 ...
## $ 2015 : num [1:266] 1.97 4.68 5.41 5.39 5.77 ...
## $ 2016 : num [1:266] 1.95 4.62 5.26 5.33 5.69 ...
## $ 2017 : num [1:266] 1.84 4.57 5.13 5.26 5.6 ...
## $ 2018 : num [1:266] 1.59 4.53 5 5.19 5.52 ...
## $ 2019 : num [1:266] 1.49 4.48 4.87 5.12 5.44 ...
## $ 2020 : num [1:266] 1.32 4.42 4.75 5.05 5.37 ...
## $ 2021 : num [1:266] 1.18 4.35 4.64 4.98 5.3 ...
## $ 2022 : logi [1:266] NA NA NA NA NA NA ...summary(fertility_rates) # Statistical summary of data. Mainly for numerics## Country Name Country Code Indicator Name Indicator Code
## Length:266 Length:266 Length:266 Length:266
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## 1960 1961 1962 1963
## Min. :1.940 Min. :1.940 Min. :1.790 Min. :1.820
## 1st Qu.:4.240 1st Qu.:4.082 1st Qu.:4.190 1st Qu.:4.160
## Median :6.078 Median :6.083 Median :6.085 Median :6.147
## Mean :5.444 Mean :5.423 Mean :5.456 Mean :5.488
## 3rd Qu.:6.721 3rd Qu.:6.725 3rd Qu.:6.747 3rd Qu.:6.771
## Max. :8.234 Max. :8.266 Max. :8.285 Max. :8.309
## NA's :13 NA's :14 NA's :13 NA's :14
## 1964 1965 1966 1967
## Min. :1.790 Min. :1.740 Min. :1.580 Min. :1.800
## 1st Qu.:4.059 1st Qu.:3.842 1st Qu.:3.709 1st Qu.:3.637
## Median :6.019 Median :5.960 Median :5.907 Median :5.825
## Mean :5.426 Mean :5.381 Mean :5.323 Mean :5.269
## 3rd Qu.:6.734 3rd Qu.:6.733 3rd Qu.:6.707 3rd Qu.:6.683
## Max. :8.330 Max. :8.344 Max. :8.356 Max. :8.340
## NA's :13 NA's :13 NA's :13 NA's :13
## 1968 1969 1970 1971
## Min. :1.830 Min. :1.870 Min. :1.823 Min. :1.680
## 1st Qu.:3.460 1st Qu.:3.284 1st Qu.:3.188 1st Qu.:3.144
## Median :5.788 Median :5.738 Median :5.636 Median :5.534
## Mean :5.227 Mean :5.173 Mean :5.115 Mean :5.057
## 3rd Qu.:6.682 3rd Qu.:6.679 3rd Qu.:6.680 3rd Qu.:6.654
## Max. :8.315 Max. :8.264 Max. :8.238 Max. :8.264
## NA's :13 NA's :13 NA's :12 NA's :12
## 1972 1973 1974 1975
## Min. :1.580 Min. :1.490 Min. :1.510 Min. :1.450
## 1st Qu.:3.060 1st Qu.:2.933 1st Qu.:2.889 1st Qu.:2.760
## Median :5.314 Median :5.252 Median :5.086 Median :4.975
## Mean :4.987 Mean :4.903 Mean :4.831 Mean :4.742
## 3rd Qu.:6.599 3rd Qu.:6.630 3rd Qu.:6.608 3rd Qu.:6.586
## Max. :8.299 Max. :8.304 Max. :8.335 Max. :8.401
## NA's :12 NA's :12 NA's :12 NA's :11
## 1976 1977 1978 1979
## Min. :1.450 Min. :1.400 Min. :1.380 Min. :1.380
## 1st Qu.:2.731 1st Qu.:2.659 1st Qu.:2.577 1st Qu.:2.554
## Median :4.810 Median :4.706 Median :4.534 Median :4.493
## Mean :4.678 Mean :4.610 Mean :4.555 Mean :4.522
## 3rd Qu.:6.554 3rd Qu.:6.512 3rd Qu.:6.474 3rd Qu.:6.463
## Max. :8.444 Max. :8.504 Max. :8.520 Max. :8.671
## NA's :10 NA's :11 NA's :11 NA's :11
## 1980 1981 1982 1983
## Min. :1.440 Min. :1.430 Min. :1.410 Min. :1.330
## 1st Qu.:2.471 1st Qu.:2.423 1st Qu.:2.384 1st Qu.:2.373
## Median :4.426 Median :4.314 Median :4.216 Median :4.095
## Mean :4.479 Mean :4.425 Mean :4.381 Mean :4.326
## 3rd Qu.:6.399 3rd Qu.:6.375 3rd Qu.:6.371 3rd Qu.:6.303
## Max. :8.710 Max. :8.752 Max. :8.793 Max. :8.828
## NA's :11 NA's :11 NA's :10 NA's :11
## 1984 1985 1986 1987
## Min. :1.290 Min. :1.370 Min. :1.350 Min. :1.311
## 1st Qu.:2.340 1st Qu.:2.324 1st Qu.:2.321 1st Qu.:2.288
## Median :4.014 Median :3.959 Median :3.962 Median :3.788
## Mean :4.270 Mean :4.216 Mean :4.164 Mean :4.103
## 3rd Qu.:6.229 3rd Qu.:6.138 3rd Qu.:6.018 3rd Qu.:5.856
## Max. :8.853 Max. :8.864 Max. :8.858 Max. :8.833
## NA's :11 NA's :11 NA's :11 NA's :10
## 1988 1989 1990 1991
## Min. :1.360 Min. :1.296 Min. :1.272 Min. :1.281
## 1st Qu.:2.280 1st Qu.:2.244 1st Qu.:2.304 1st Qu.:2.187
## Median :3.711 Median :3.583 Median :3.471 Median :3.402
## Mean :4.050 Mean :3.983 Mean :3.931 Mean :3.853
## 3rd Qu.:5.798 3rd Qu.:5.692 3rd Qu.:5.604 3rd Qu.:5.474
## Max. :8.786 Max. :8.713 Max. :8.606 Max. :8.459
## NA's :11 NA's :11 NA's :9 NA's :10
## 1992 1993 1994 1995
## Min. :1.290 Min. :1.250 Min. :1.190 Min. :1.160
## 1st Qu.:2.120 1st Qu.:2.034 1st Qu.:1.987 1st Qu.:1.967
## Median :3.318 Median :3.208 Median :3.121 Median :3.072
## Mean :3.776 Mean :3.699 Mean :3.623 Mean :3.543
## 3rd Qu.:5.314 3rd Qu.:5.203 3rd Qu.:5.046 3rd Qu.:4.884
## Max. :8.272 Max. :8.048 Max. :7.989 Max. :7.962
## NA's :9 NA's :10 NA's :10 NA's :9
## 1996 1997 1998 1999
## Min. :1.140 Min. :1.090 Min. :1.016 Min. :0.981
## 1st Qu.:1.931 1st Qu.:1.896 1st Qu.:1.860 1st Qu.:1.817
## Median :3.014 Median :2.940 Median :2.815 Median :2.767
## Mean :3.476 Mean :3.408 Mean :3.339 Mean :3.288
## 3rd Qu.:4.771 3rd Qu.:4.660 3rd Qu.:4.576 3rd Qu.:4.516
## Max. :7.985 Max. :7.965 Max. :7.817 Max. :7.752
## NA's :10 NA's :9 NA's :9 NA's :9
## 2000 2001 2002 2003
## Min. :0.912 Min. :0.840 Min. :0.800 Min. :0.792
## 1st Qu.:1.855 1st Qu.:1.800 1st Qu.:1.790 1st Qu.:1.786
## Median :2.716 Median :2.667 Median :2.623 Median :2.583
## Mean :3.235 Mean :3.182 Mean :3.136 Mean :3.095
## 3rd Qu.:4.434 3rd Qu.:4.375 3rd Qu.:4.304 3rd Qu.:4.234
## Max. :7.732 Max. :7.695 Max. :7.671 Max. :7.654
## NA's :7 NA's :8 NA's :8 NA's :8
## 2004 2005 2006 2007
## Min. :0.800 Min. :0.834 Min. :0.874 Min. :0.918
## 1st Qu.:1.781 1st Qu.:1.786 1st Qu.:1.793 1st Qu.:1.823
## Median :2.582 Median :2.552 Median :2.503 Median :2.510
## Mean :3.068 Mean :3.033 Mean :3.007 Mean :2.991
## 3rd Qu.:4.129 3rd Qu.:3.983 3rd Qu.:3.850 3rd Qu.:3.863
## Max. :7.634 Max. :7.615 Max. :7.579 Max. :7.559
## NA's :8 NA's :7 NA's :7 NA's :7
## 2008 2009 2010 2011
## Min. :0.947 Min. :0.986 Min. :1.042 Min. :1.115
## 1st Qu.:1.838 1st Qu.:1.823 1st Qu.:1.802 1st Qu.:1.781
## Median :2.481 Median :2.437 Median :2.397 Median :2.334
## Mean :2.983 Mean :2.957 Mean :2.924 Mean :2.895
## 3rd Qu.:3.877 3rd Qu.:3.865 3rd Qu.:3.880 3rd Qu.:3.836
## Max. :7.539 Max. :7.513 Max. :7.485 Max. :7.449
## NA's :7 NA's :7 NA's :7 NA's :8
## 2012 2013 2014 2015
## Min. :1.103 Min. :1.080 Min. :1.205 Min. :1.186
## 1st Qu.:1.792 1st Qu.:1.750 1st Qu.:1.751 1st Qu.:1.734
## Median :2.312 Median :2.328 Median :2.300 Median :2.260
## Mean :2.868 Mean :2.835 Mean :2.813 Mean :2.775
## 3rd Qu.:3.753 3rd Qu.:3.688 3rd Qu.:3.632 3rd Qu.:3.560
## Max. :7.400 Max. :7.344 Max. :7.279 Max. :7.211
## NA's :6 NA's :8 NA's :8 NA's :7
## 2016 2017 2018 2019
## Min. :0.987 Min. :0.872 Min. :0.917 Min. :0.918
## 1st Qu.:1.725 1st Qu.:1.693 1st Qu.:1.648 1st Qu.:1.612
## Median :2.245 Median :2.211 Median :2.175 Median :2.139
## Mean :2.742 Mean :2.694 Mean :2.653 Mean :2.611
## 3rd Qu.:3.492 3rd Qu.:3.432 3rd Qu.:3.403 3rd Qu.:3.333
## Max. :7.141 Max. :7.084 Max. :7.023 Max. :6.961
## NA's :8 NA's :8 NA's :8 NA's :8
## 2020 2021 2022
## Min. :0.837 Min. :0.772 Mode:logical
## 1st Qu.:1.572 1st Qu.:1.583 NA's:266
## Median :2.103 Median :2.088
## Mean :2.560 Mean :2.542
## 3rd Qu.:3.271 3rd Qu.:3.288
## Max. :6.892 Max. :6.820
## NA's :7 NA's :8# delete two columns: "Indicator Name" and "Indicator Code"
fertility_rates <- fertility_rates %>%
select(-c("Indicator Name", "Indicator Code"))Step 3: Clean up data and prepare for analysis
Inspect the new table that has been created
colnames(fertility_rates) # Column names## [1] "Country Name" "Country Code" "1960" "1961" "1962"
## [6] "1963" "1964" "1965" "1966" "1967"
## [11] "1968" "1969" "1970" "1971" "1972"
## [16] "1973" "1974" "1975" "1976" "1977"
## [21] "1978" "1979" "1980" "1981" "1982"
## [26] "1983" "1984" "1985" "1986" "1987"
## [31] "1988" "1989" "1990" "1991" "1992"
## [36] "1993" "1994" "1995" "1996" "1997"
## [41] "1998" "1999" "2000" "2001" "2002"
## [46] "2003" "2004" "2005" "2006" "2007"
## [51] "2008" "2009" "2010" "2011" "2012"
## [56] "2013" "2014" "2015" "2016" "2017"
## [61] "2018" "2019" "2020" "2021" "2022"nrow(fertility_rates) # Number of rows## [1] 266dim(fertility_rates) # Dimensions## [1] 266 65head(fertility_rates) # First 6 rows of data frame## # A tibble: 6 × 65
## `Country Name` `Country Code` `1960` `1961` `1962` `1963` `1964` `1965` `1966`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba ABW 4.82 4.66 4.47 4.27 4.06 3.84 3.62
## 2 Africa Easter… AFE 6.72 6.74 6.76 6.78 6.79 6.80 6.81
## 3 Afghanistan AFG 7.28 7.28 7.29 7.30 7.30 7.30 7.32
## 4 Africa Wester… AFW 6.46 6.47 6.49 6.51 6.53 6.54 6.56
## 5 Angola AGO 6.71 6.79 6.87 6.95 7.04 7.12 7.19
## 6 Albania ALB 6.46 6.35 6.21 6.05 5.85 5.62 5.46
## # ℹ 56 more variables: `1967` <dbl>, `1968` <dbl>, `1969` <dbl>, `1970` <dbl>,
## # `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>, `1975` <dbl>,
## # `1976` <dbl>, `1977` <dbl>, `1978` <dbl>, `1979` <dbl>, `1980` <dbl>,
## # `1981` <dbl>, `1982` <dbl>, `1983` <dbl>, `1984` <dbl>, `1985` <dbl>,
## # `1986` <dbl>, `1987` <dbl>, `1988` <dbl>, `1989` <dbl>, `1990` <dbl>,
## # `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>, `1995` <dbl>,
## # `1996` <dbl>, `1997` <dbl>, `1998` <dbl>, `1999` <dbl>, `2000` <dbl>, …str(fertility_rates) # Columns and respective data types (numeric, character, etc)## tibble [266 × 65] (S3: tbl_df/tbl/data.frame)
## $ Country Name: chr [1:266] "Aruba" "Africa Eastern and Southern" "Afghanistan" "Africa Western and Central" ...
## $ Country Code: chr [1:266] "ABW" "AFE" "AFG" "AFW" ...
## $ 1960 : num [1:266] 4.82 6.72 7.28 6.46 6.71 ...
## $ 1961 : num [1:266] 4.66 6.74 7.28 6.47 6.79 ...
## $ 1962 : num [1:266] 4.47 6.76 7.29 6.49 6.87 ...
## $ 1963 : num [1:266] 4.27 6.78 7.3 6.51 6.95 ...
## $ 1964 : num [1:266] 4.06 6.79 7.3 6.53 7.04 ...
## $ 1965 : num [1:266] 3.84 6.8 7.3 6.54 7.12 ...
## $ 1966 : num [1:266] 3.62 6.81 7.32 6.56 7.19 ...
## $ 1967 : num [1:266] 3.42 6.82 7.34 6.59 7.27 ...
## $ 1968 : num [1:266] 3.23 6.83 7.36 6.61 7.33 ...
## $ 1969 : num [1:266] 3.05 6.83 7.39 6.64 7.39 ...
## $ 1970 : num [1:266] 2.91 6.84 7.4 6.66 7.43 ...
## $ 1971 : num [1:266] 2.79 6.84 7.43 6.7 7.47 ...
## $ 1972 : num [1:266] 2.69 6.84 7.45 6.73 7.49 ...
## $ 1973 : num [1:266] 2.61 6.83 7.49 6.76 7.5 ...
## $ 1974 : num [1:266] 2.55 6.82 7.53 6.8 7.5 ...
## $ 1975 : num [1:266] 2.51 6.81 7.54 6.84 7.49 ...
## $ 1976 : num [1:266] 2.47 6.79 7.56 6.86 7.49 ...
## $ 1977 : num [1:266] 2.45 6.77 7.59 6.9 7.47 ...
## $ 1978 : num [1:266] 2.42 6.75 7.6 6.92 7.47 ...
## $ 1979 : num [1:266] 2.41 6.73 7.61 6.91 7.46 ...
## $ 1980 : num [1:266] 2.39 6.7 7.59 6.9 7.46 ...
## $ 1981 : num [1:266] 2.38 6.67 7.57 6.88 7.46 ...
## $ 1982 : num [1:266] 2.36 6.64 7.55 6.86 7.46 ...
## $ 1983 : num [1:266] 2.35 6.6 7.54 6.83 7.46 ...
## $ 1984 : num [1:266] 2.34 6.57 7.51 6.78 7.46 ...
## $ 1985 : num [1:266] 2.33 6.51 7.52 6.73 7.45 ...
## $ 1986 : num [1:266] 2.32 6.46 7.52 6.68 7.43 ...
## $ 1987 : num [1:266] 2.31 6.42 7.53 6.64 7.41 ...
## $ 1988 : num [1:266] 2.29 6.34 7.53 6.6 7.37 ...
## $ 1989 : num [1:266] 2.27 6.26 7.53 6.57 7.33 ...
## $ 1990 : num [1:266] 2.3 6.17 7.57 6.52 7.27 ...
## $ 1991 : num [1:266] 2.31 6.1 7.61 6.47 7.21 ...
## $ 1992 : num [1:266] 2.28 6.03 7.67 6.42 7.14 ...
## $ 1993 : num [1:266] 2.23 5.96 7.72 6.36 7.07 ...
## $ 1994 : num [1:266] 2.12 5.9 7.72 6.3 6.99 ...
## $ 1995 : num [1:266] 2.19 5.84 7.71 6.24 6.92 ...
## $ 1996 : num [1:266] 2.15 5.77 7.71 6.17 6.85 ...
## $ 1997 : num [1:266] 2.14 5.7 7.67 6.1 6.79 ...
## $ 1998 : num [1:266] 1.96 5.64 7.64 6.04 6.73 ...
## $ 1999 : num [1:266] 1.87 5.59 7.6 6.03 6.68 ...
## $ 2000 : num [1:266] 1.9 5.52 7.53 6.02 6.64 ...
## $ 2001 : num [1:266] 1.83 5.48 7.45 6 6.6 ...
## $ 2002 : num [1:266] 1.76 5.43 7.34 5.97 6.57 ...
## $ 2003 : num [1:266] 1.75 5.38 7.22 5.93 6.53 ...
## $ 2004 : num [1:266] 1.68 5.34 7.07 5.89 6.5 ...
## $ 2005 : num [1:266] 1.78 5.31 6.91 5.86 6.46 ...
## $ 2006 : num [1:266] 1.91 5.27 6.72 5.85 6.42 ...
## $ 2007 : num [1:266] 1.93 5.22 6.53 5.82 6.37 ...
## $ 2008 : num [1:266] 1.94 5.19 6.38 5.79 6.32 ...
## $ 2009 : num [1:266] 1.92 5.12 6.24 5.75 6.26 ...
## $ 2010 : num [1:266] 1.94 5.04 6.1 5.7 6.19 ...
## $ 2011 : num [1:266] 1.96 4.96 5.96 5.65 6.12 ...
## $ 2012 : num [1:266] 2.03 4.88 5.83 5.58 6.04 ...
## $ 2013 : num [1:266] 2.12 4.81 5.7 5.51 5.95 ...
## $ 2014 : num [1:266] 2.15 4.74 5.56 5.44 5.86 ...
## $ 2015 : num [1:266] 1.97 4.68 5.41 5.39 5.77 ...
## $ 2016 : num [1:266] 1.95 4.62 5.26 5.33 5.69 ...
## $ 2017 : num [1:266] 1.84 4.57 5.13 5.26 5.6 ...
## $ 2018 : num [1:266] 1.59 4.53 5 5.19 5.52 ...
## $ 2019 : num [1:266] 1.49 4.48 4.87 5.12 5.44 ...
## $ 2020 : num [1:266] 1.32 4.42 4.75 5.05 5.37 ...
## $ 2021 : num [1:266] 1.18 4.35 4.64 4.98 5.3 ...
## $ 2022 : logi [1:266] NA NA NA NA NA NA ...summary(fertility_rates) # Statistical summary of data. Mainly for numerics## Country Name Country Code 1960 1961
## Length:266 Length:266 Min. :1.940 Min. :1.940
## Class :character Class :character 1st Qu.:4.240 1st Qu.:4.082
## Mode :character Mode :character Median :6.078 Median :6.083
## Mean :5.444 Mean :5.423
## 3rd Qu.:6.721 3rd Qu.:6.725
## Max. :8.234 Max. :8.266
## NA's :13 NA's :14
## 1962 1963 1964 1965
## Min. :1.790 Min. :1.820 Min. :1.790 Min. :1.740
## 1st Qu.:4.190 1st Qu.:4.160 1st Qu.:4.059 1st Qu.:3.842
## Median :6.085 Median :6.147 Median :6.019 Median :5.960
## Mean :5.456 Mean :5.488 Mean :5.426 Mean :5.381
## 3rd Qu.:6.747 3rd Qu.:6.771 3rd Qu.:6.734 3rd Qu.:6.733
## Max. :8.285 Max. :8.309 Max. :8.330 Max. :8.344
## NA's :13 NA's :14 NA's :13 NA's :13
## 1966 1967 1968 1969
## Min. :1.580 Min. :1.800 Min. :1.830 Min. :1.870
## 1st Qu.:3.709 1st Qu.:3.637 1st Qu.:3.460 1st Qu.:3.284
## Median :5.907 Median :5.825 Median :5.788 Median :5.738
## Mean :5.323 Mean :5.269 Mean :5.227 Mean :5.173
## 3rd Qu.:6.707 3rd Qu.:6.683 3rd Qu.:6.682 3rd Qu.:6.679
## Max. :8.356 Max. :8.340 Max. :8.315 Max. :8.264
## NA's :13 NA's :13 NA's :13 NA's :13
## 1970 1971 1972 1973
## Min. :1.823 Min. :1.680 Min. :1.580 Min. :1.490
## 1st Qu.:3.188 1st Qu.:3.144 1st Qu.:3.060 1st Qu.:2.933
## Median :5.636 Median :5.534 Median :5.314 Median :5.252
## Mean :5.115 Mean :5.057 Mean :4.987 Mean :4.903
## 3rd Qu.:6.680 3rd Qu.:6.654 3rd Qu.:6.599 3rd Qu.:6.630
## Max. :8.238 Max. :8.264 Max. :8.299 Max. :8.304
## NA's :12 NA's :12 NA's :12 NA's :12
## 1974 1975 1976 1977
## Min. :1.510 Min. :1.450 Min. :1.450 Min. :1.400
## 1st Qu.:2.889 1st Qu.:2.760 1st Qu.:2.731 1st Qu.:2.659
## Median :5.086 Median :4.975 Median :4.810 Median :4.706
## Mean :4.831 Mean :4.742 Mean :4.678 Mean :4.610
## 3rd Qu.:6.608 3rd Qu.:6.586 3rd Qu.:6.554 3rd Qu.:6.512
## Max. :8.335 Max. :8.401 Max. :8.444 Max. :8.504
## NA's :12 NA's :11 NA's :10 NA's :11
## 1978 1979 1980 1981
## Min. :1.380 Min. :1.380 Min. :1.440 Min. :1.430
## 1st Qu.:2.577 1st Qu.:2.554 1st Qu.:2.471 1st Qu.:2.423
## Median :4.534 Median :4.493 Median :4.426 Median :4.314
## Mean :4.555 Mean :4.522 Mean :4.479 Mean :4.425
## 3rd Qu.:6.474 3rd Qu.:6.463 3rd Qu.:6.399 3rd Qu.:6.375
## Max. :8.520 Max. :8.671 Max. :8.710 Max. :8.752
## NA's :11 NA's :11 NA's :11 NA's :11
## 1982 1983 1984 1985
## Min. :1.410 Min. :1.330 Min. :1.290 Min. :1.370
## 1st Qu.:2.384 1st Qu.:2.373 1st Qu.:2.340 1st Qu.:2.324
## Median :4.216 Median :4.095 Median :4.014 Median :3.959
## Mean :4.381 Mean :4.326 Mean :4.270 Mean :4.216
## 3rd Qu.:6.371 3rd Qu.:6.303 3rd Qu.:6.229 3rd Qu.:6.138
## Max. :8.793 Max. :8.828 Max. :8.853 Max. :8.864
## NA's :10 NA's :11 NA's :11 NA's :11
## 1986 1987 1988 1989
## Min. :1.350 Min. :1.311 Min. :1.360 Min. :1.296
## 1st Qu.:2.321 1st Qu.:2.288 1st Qu.:2.280 1st Qu.:2.244
## Median :3.962 Median :3.788 Median :3.711 Median :3.583
## Mean :4.164 Mean :4.103 Mean :4.050 Mean :3.983
## 3rd Qu.:6.018 3rd Qu.:5.856 3rd Qu.:5.798 3rd Qu.:5.692
## Max. :8.858 Max. :8.833 Max. :8.786 Max. :8.713
## NA's :11 NA's :10 NA's :11 NA's :11
## 1990 1991 1992 1993
## Min. :1.272 Min. :1.281 Min. :1.290 Min. :1.250
## 1st Qu.:2.304 1st Qu.:2.187 1st Qu.:2.120 1st Qu.:2.034
## Median :3.471 Median :3.402 Median :3.318 Median :3.208
## Mean :3.931 Mean :3.853 Mean :3.776 Mean :3.699
## 3rd Qu.:5.604 3rd Qu.:5.474 3rd Qu.:5.314 3rd Qu.:5.203
## Max. :8.606 Max. :8.459 Max. :8.272 Max. :8.048
## NA's :9 NA's :10 NA's :9 NA's :10
## 1994 1995 1996 1997
## Min. :1.190 Min. :1.160 Min. :1.140 Min. :1.090
## 1st Qu.:1.987 1st Qu.:1.967 1st Qu.:1.931 1st Qu.:1.896
## Median :3.121 Median :3.072 Median :3.014 Median :2.940
## Mean :3.623 Mean :3.543 Mean :3.476 Mean :3.408
## 3rd Qu.:5.046 3rd Qu.:4.884 3rd Qu.:4.771 3rd Qu.:4.660
## Max. :7.989 Max. :7.962 Max. :7.985 Max. :7.965
## NA's :10 NA's :9 NA's :10 NA's :9
## 1998 1999 2000 2001
## Min. :1.016 Min. :0.981 Min. :0.912 Min. :0.840
## 1st Qu.:1.860 1st Qu.:1.817 1st Qu.:1.855 1st Qu.:1.800
## Median :2.815 Median :2.767 Median :2.716 Median :2.667
## Mean :3.339 Mean :3.288 Mean :3.235 Mean :3.182
## 3rd Qu.:4.576 3rd Qu.:4.516 3rd Qu.:4.434 3rd Qu.:4.375
## Max. :7.817 Max. :7.752 Max. :7.732 Max. :7.695
## NA's :9 NA's :9 NA's :7 NA's :8
## 2002 2003 2004 2005
## Min. :0.800 Min. :0.792 Min. :0.800 Min. :0.834
## 1st Qu.:1.790 1st Qu.:1.786 1st Qu.:1.781 1st Qu.:1.786
## Median :2.623 Median :2.583 Median :2.582 Median :2.552
## Mean :3.136 Mean :3.095 Mean :3.068 Mean :3.033
## 3rd Qu.:4.304 3rd Qu.:4.234 3rd Qu.:4.129 3rd Qu.:3.983
## Max. :7.671 Max. :7.654 Max. :7.634 Max. :7.615
## NA's :8 NA's :8 NA's :8 NA's :7
## 2006 2007 2008 2009
## Min. :0.874 Min. :0.918 Min. :0.947 Min. :0.986
## 1st Qu.:1.793 1st Qu.:1.823 1st Qu.:1.838 1st Qu.:1.823
## Median :2.503 Median :2.510 Median :2.481 Median :2.437
## Mean :3.007 Mean :2.991 Mean :2.983 Mean :2.957
## 3rd Qu.:3.850 3rd Qu.:3.863 3rd Qu.:3.877 3rd Qu.:3.865
## Max. :7.579 Max. :7.559 Max. :7.539 Max. :7.513
## NA's :7 NA's :7 NA's :7 NA's :7
## 2010 2011 2012 2013
## Min. :1.042 Min. :1.115 Min. :1.103 Min. :1.080
## 1st Qu.:1.802 1st Qu.:1.781 1st Qu.:1.792 1st Qu.:1.750
## Median :2.397 Median :2.334 Median :2.312 Median :2.328
## Mean :2.924 Mean :2.895 Mean :2.868 Mean :2.835
## 3rd Qu.:3.880 3rd Qu.:3.836 3rd Qu.:3.753 3rd Qu.:3.688
## Max. :7.485 Max. :7.449 Max. :7.400 Max. :7.344
## NA's :7 NA's :8 NA's :6 NA's :8
## 2014 2015 2016 2017
## Min. :1.205 Min. :1.186 Min. :0.987 Min. :0.872
## 1st Qu.:1.751 1st Qu.:1.734 1st Qu.:1.725 1st Qu.:1.693
## Median :2.300 Median :2.260 Median :2.245 Median :2.211
## Mean :2.813 Mean :2.775 Mean :2.742 Mean :2.694
## 3rd Qu.:3.632 3rd Qu.:3.560 3rd Qu.:3.492 3rd Qu.:3.432
## Max. :7.279 Max. :7.211 Max. :7.141 Max. :7.084
## NA's :8 NA's :7 NA's :8 NA's :8
## 2018 2019 2020 2021 2022
## Min. :0.917 Min. :0.918 Min. :0.837 Min. :0.772 Mode:logical
## 1st Qu.:1.648 1st Qu.:1.612 1st Qu.:1.572 1st Qu.:1.583 NA's:266
## Median :2.175 Median :2.139 Median :2.103 Median :2.088
## Mean :2.653 Mean :2.611 Mean :2.560 Mean :2.542
## 3rd Qu.:3.403 3rd Qu.:3.333 3rd Qu.:3.271 3rd Qu.:3.288
## Max. :7.023 Max. :6.961 Max. :6.892 Max. :6.820
## NA's :8 NA's :8 NA's :7 NA's :8Step 4: Conduct descriptive analysis
summary(fertility_rates$"1960")## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.940 4.240 6.078 5.444 6.721 8.234 13# Compare Average Total Fertility rates in 1960 vs. Average Total Fertility rates in 2021
aggregate(fertility_rates$"1960" ~ fertility_rates$"2021", FUN = mean)## fertility_rates$"2021" fertility_rates$"1960"
## 1 0.772000 5.067000
## 2 0.808000 5.949000
## 3 0.907000 4.796000
## 4 1.005000 5.162000
## 5 1.088000 4.933000
## 6 1.120000 5.760000
## 7 1.140000 3.620000
## 8 1.160000 2.240000
## 9 1.164000 4.451000
## 10 1.180000 4.820000
## 11 1.190000 2.860000
## 12 1.250000 2.400000
## 13 1.300000 2.755000
## 14 1.321000 3.512000
## 15 1.330000 2.980000
## 16 1.331000 6.248000
## 17 1.340000 2.560000
## 18 1.350000 3.907000
## 19 1.352000 5.580000
## 20 1.380000 2.725000
## 21 1.383908 4.801000
## 22 1.389000 4.818000
## 23 1.390000 4.342500
## 24 1.396865 4.378614
## 25 1.399000 6.967000
## 26 1.410000 6.167000
## 27 1.413000 6.704000
## 28 1.430000 3.811000
## 29 1.442000 4.129000
## 30 1.457000 2.270000
## 31 1.460000 4.719000
## 32 1.480000 2.401500
## 33 1.483000 2.670000
## 34 1.488552 2.897990
## 35 1.489433 4.561606
## 36 1.493000 2.673500
## 37 1.498219 4.531774
## 38 1.504510 2.605864
## 39 1.516846 4.864229
## 40 1.520000 4.159000
## 41 1.520288 2.571186
## 42 1.520442 4.841982
## 43 1.522000 6.359000
## 44 1.531000 7.169000
## 45 1.533000 6.712000
## 46 1.537000 4.697000
## 47 1.544788 3.019437
## 48 1.550000 2.850000
## 49 1.550993 2.491059
## 50 1.560000 2.690000
## 51 1.564000 2.875000
## 52 1.570000 1.940000
## 53 1.575000 4.786000
## 54 1.580000 3.094000
## 55 1.581000 4.202000
## 56 1.589351 3.285589
## 57 1.590000 2.020000
## 58 1.595000 6.866000
## 59 1.600000 3.253500
## 60 1.610000 1.980000
## 61 1.620000 2.676000
## 62 1.626000 5.345000
## 63 1.633000 4.333000
## 64 1.640000 3.155000
## 65 1.640018 3.668255
## 66 1.641000 6.061000
## 67 1.664000 3.654000
## 68 1.664522 5.727913
## 69 1.669000 5.790000
## 70 1.670000 2.170000
## 71 1.692000 7.052500
## 72 1.693061 2.831429
## 73 1.699000 5.888000
## 74 1.700000 3.453000
## 75 1.717000 6.735000
## 76 1.720000 3.175000
## 77 1.750000 3.498000
## 78 1.778000 6.836000
## 79 1.797000 7.286000
## 80 1.800000 2.340000
## 81 1.801000 6.647000
## 82 1.803000 6.520500
## 83 1.806000 3.328000
## 84 1.809000 3.568000
## 85 1.811000 7.152000
## 86 1.820000 4.290000
## 87 1.822000 6.763000
## 88 1.824446 4.970095
## 89 1.830000 2.470000
## 90 1.835001 3.103683
## 91 1.848000 3.012000
## 92 1.852014 5.959449
## 93 1.853283 5.865889
## 94 1.864022 5.945441
## 95 1.883205 3.184230
## 96 1.885000 3.075000
## 97 1.889000 6.383000
## 98 1.896000 6.885000
## 99 1.944000 6.280000
## 100 1.981000 6.784000
## 101 1.990000 5.465000
## 102 2.004000 6.743000
## 103 2.010000 5.972500
## 104 2.020000 6.278000
## 105 2.026000 6.721000
## 106 2.029000 6.030000
## 107 2.031000 5.921000
## 108 2.081000 2.942000
## 109 2.086000 6.942000
## 110 2.091000 5.818000
## 111 2.110000 7.162000
## 112 2.136727 5.219097
## 113 2.151000 5.983000
## 114 2.175000 5.547000
## 115 2.192000 6.941000
## 116 2.211000 6.358000
## 117 2.240930 6.078350
## 118 2.273000 7.555000
## 119 2.273169 4.695876
## 120 2.321000 7.159000
## 121 2.325000 5.844000
## 122 2.328000 7.040000
## 123 2.344000 6.251000
## 124 2.348000 6.608000
## 125 2.350962 6.166562
## 126 2.363000 7.458000
## 127 2.374000 6.159000
## 128 2.384585 5.251170
## 129 2.393171 5.301390
## 130 2.395000 6.955000
## 131 2.397000 6.372000
## 132 2.415000 5.948000
## 133 2.427000 7.626000
## 134 2.462000 7.373000
## 135 2.469000 6.500000
## 136 2.475000 6.461000
## 137 2.496000 6.292000
## 138 2.569000 5.906000
## 139 2.578281 5.983878
## 140 2.618000 6.358000
## 141 2.623000 7.247000
## 142 2.629064 6.950247
## 143 2.655901 5.357909
## 144 2.660823 7.002555
## 145 2.667000 6.590000
## 146 2.670537 7.002555
## 147 2.711000 6.686000
## 148 2.729000 8.234000
## 149 2.747000 7.485000
## 150 2.748000 7.148000
## 151 2.791000 6.628000
## 152 2.804000 6.828000
## 153 2.814000 6.208000
## 154 2.830000 7.669000
## 155 2.837000 6.827000
## 156 2.839000 6.752000
## 157 2.859614 5.069770
## 158 2.889000 7.503000
## 159 2.890000 5.376000
## 160 2.917000 6.794000
## 161 3.000000 3.866000
## 162 3.018000 5.819000
## 163 3.142565 6.934332
## 164 3.149000 6.319000
## 165 3.163000 4.778000
## 166 3.173000 6.613000
## 167 3.186000 6.547000
## 168 3.215000 6.018000
## 169 3.237000 6.885000
## 170 3.241013 6.768236
## 171 3.303000 6.205000
## 172 3.304000 6.553000
## 173 3.320000 4.530000
## 174 3.335000 7.632000
## 175 3.470000 6.800000
## 176 3.491000 5.821000
## 177 3.496000 5.300000
## 178 3.519000 5.269000
## 179 3.563000 6.847000
## 180 3.735000 6.863000
## 181 3.795000 7.938000
## 182 3.821000 8.187000
## 183 3.823000 6.242000
## 184 3.843961 6.629390
## 185 3.851000 7.300000
## 186 3.867000 6.483000
## 187 3.917000 7.029000
## 188 3.921027 6.622652
## 189 3.930000 7.646000
## 190 3.978000 6.483500
## 191 3.983000 6.970000
## 192 3.983665 6.640794
## 193 4.005000 5.921000
## 194 4.080936 6.607081
## 195 4.089000 6.391000
## 196 4.159000 6.880000
## 197 4.171000 6.085000
## 198 4.257000 6.717000
## 199 4.266000 5.653000
## 200 4.308000 7.115000
## 201 4.354710 6.724125
## 202 4.359060 5.603609
## 203 4.387000 6.996000
## 204 4.398000 6.354000
## 205 4.399000 6.112000
## 206 4.418000 7.691000
## 207 4.457000 6.647000
## 208 4.463000 5.647000
## 209 4.469000 6.721000
## 210 4.585000 6.936000
## 211 4.601289 6.609096
## 212 4.601463 6.609096
## 213 4.620300 6.702820
## 214 4.643000 7.282000
## 215 4.644000 6.315000
## 216 4.644854 6.414706
## 217 4.684000 6.246000
## 218 4.726000 6.725000
## 219 4.772000 6.248000
## 220 4.844171 6.563645
## 221 4.973000 6.282000
## 222 4.978662 6.458448
## 223 5.078000 7.003000
## 224 5.237000 6.364000
## 225 5.304000 6.708000
## 226 5.956000 7.004000
## 227 5.978000 5.814000
## 228 6.156000 6.080000
## 229 6.255000 6.250000
## 230 6.312000 7.250000
## 231 6.820000 7.530000# Compare median fertility rates in 1960 vs. median fertility in 2021
aggregate(fertility_rates$"1960" ~ fertility_rates$"2021", FUN = median)## fertility_rates$"2021" fertility_rates$"1960"
## 1 0.772000 5.067000
## 2 0.808000 5.949000
## 3 0.907000 4.796000
## 4 1.005000 5.162000
## 5 1.088000 4.933000
## 6 1.120000 5.760000
## 7 1.140000 3.620000
## 8 1.160000 2.240000
## 9 1.164000 4.451000
## 10 1.180000 4.820000
## 11 1.190000 2.860000
## 12 1.250000 2.400000
## 13 1.300000 2.755000
## 14 1.321000 3.512000
## 15 1.330000 2.980000
## 16 1.331000 6.248000
## 17 1.340000 2.560000
## 18 1.350000 3.907000
## 19 1.352000 5.580000
## 20 1.380000 2.725000
## 21 1.383908 4.801000
## 22 1.389000 4.818000
## 23 1.390000 4.342500
## 24 1.396865 4.378614
## 25 1.399000 6.967000
## 26 1.410000 6.167000
## 27 1.413000 6.704000
## 28 1.430000 3.811000
## 29 1.442000 4.129000
## 30 1.457000 2.270000
## 31 1.460000 4.719000
## 32 1.480000 2.401500
## 33 1.483000 2.670000
## 34 1.488552 2.897990
## 35 1.489433 4.561606
## 36 1.493000 2.673500
## 37 1.498219 4.531774
## 38 1.504510 2.605864
## 39 1.516846 4.864229
## 40 1.520000 4.159000
## 41 1.520288 2.571186
## 42 1.520442 4.841982
## 43 1.522000 6.359000
## 44 1.531000 7.169000
## 45 1.533000 6.712000
## 46 1.537000 4.697000
## 47 1.544788 3.019437
## 48 1.550000 2.850000
## 49 1.550993 2.491059
## 50 1.560000 2.690000
## 51 1.564000 2.875000
## 52 1.570000 1.940000
## 53 1.575000 4.786000
## 54 1.580000 2.370000
## 55 1.581000 4.202000
## 56 1.589351 3.285589
## 57 1.590000 2.020000
## 58 1.595000 6.866000
## 59 1.600000 3.253500
## 60 1.610000 1.980000
## 61 1.620000 2.676000
## 62 1.626000 5.345000
## 63 1.633000 4.333000
## 64 1.640000 3.040000
## 65 1.640018 3.668255
## 66 1.641000 6.061000
## 67 1.664000 3.654000
## 68 1.664522 5.727913
## 69 1.669000 5.790000
## 70 1.670000 2.170000
## 71 1.692000 7.052500
## 72 1.693061 2.831429
## 73 1.699000 5.888000
## 74 1.700000 3.453000
## 75 1.717000 6.735000
## 76 1.720000 3.175000
## 77 1.750000 3.498000
## 78 1.778000 6.836000
## 79 1.797000 7.286000
## 80 1.800000 2.340000
## 81 1.801000 6.647000
## 82 1.803000 6.520500
## 83 1.806000 3.328000
## 84 1.809000 3.568000
## 85 1.811000 7.152000
## 86 1.820000 4.290000
## 87 1.822000 6.763000
## 88 1.824446 4.970095
## 89 1.830000 2.470000
## 90 1.835001 3.103683
## 91 1.848000 3.012000
## 92 1.852014 5.959449
## 93 1.853283 5.865889
## 94 1.864022 5.945441
## 95 1.883205 3.184230
## 96 1.885000 3.075000
## 97 1.889000 6.383000
## 98 1.896000 6.885000
## 99 1.944000 6.280000
## 100 1.981000 6.784000
## 101 1.990000 5.465000
## 102 2.004000 6.743000
## 103 2.010000 5.972500
## 104 2.020000 6.278000
## 105 2.026000 6.721000
## 106 2.029000 6.030000
## 107 2.031000 5.921000
## 108 2.081000 2.942000
## 109 2.086000 6.942000
## 110 2.091000 5.818000
## 111 2.110000 7.162000
## 112 2.136727 5.219097
## 113 2.151000 5.983000
## 114 2.175000 5.547000
## 115 2.192000 6.941000
## 116 2.211000 6.358000
## 117 2.240930 6.078350
## 118 2.273000 7.555000
## 119 2.273169 4.695876
## 120 2.321000 7.159000
## 121 2.325000 5.844000
## 122 2.328000 7.040000
## 123 2.344000 6.251000
## 124 2.348000 6.608000
## 125 2.350962 6.166562
## 126 2.363000 7.458000
## 127 2.374000 6.159000
## 128 2.384585 5.251170
## 129 2.393171 5.301390
## 130 2.395000 6.955000
## 131 2.397000 6.372000
## 132 2.415000 5.948000
## 133 2.427000 7.626000
## 134 2.462000 7.373000
## 135 2.469000 6.500000
## 136 2.475000 6.461000
## 137 2.496000 6.292000
## 138 2.569000 5.906000
## 139 2.578281 5.983878
## 140 2.618000 6.358000
## 141 2.623000 7.247000
## 142 2.629064 6.950247
## 143 2.655901 5.357909
## 144 2.660823 7.002555
## 145 2.667000 6.590000
## 146 2.670537 7.002555
## 147 2.711000 6.686000
## 148 2.729000 8.234000
## 149 2.747000 7.485000
## 150 2.748000 7.148000
## 151 2.791000 6.628000
## 152 2.804000 6.828000
## 153 2.814000 6.208000
## 154 2.830000 7.669000
## 155 2.837000 6.827000
## 156 2.839000 6.752000
## 157 2.859614 5.069770
## 158 2.889000 7.503000
## 159 2.890000 5.376000
## 160 2.917000 6.794000
## 161 3.000000 3.866000
## 162 3.018000 5.819000
## 163 3.142565 6.934332
## 164 3.149000 6.319000
## 165 3.163000 4.778000
## 166 3.173000 6.613000
## 167 3.186000 6.547000
## 168 3.215000 6.018000
## 169 3.237000 6.885000
## 170 3.241013 6.768236
## 171 3.303000 6.205000
## 172 3.304000 6.553000
## 173 3.320000 4.530000
## 174 3.335000 7.632000
## 175 3.470000 6.800000
## 176 3.491000 5.821000
## 177 3.496000 5.300000
## 178 3.519000 5.269000
## 179 3.563000 6.847000
## 180 3.735000 6.863000
## 181 3.795000 7.938000
## 182 3.821000 8.187000
## 183 3.823000 6.242000
## 184 3.843961 6.629390
## 185 3.851000 7.300000
## 186 3.867000 6.483000
## 187 3.917000 7.029000
## 188 3.921027 6.622652
## 189 3.930000 7.646000
## 190 3.978000 6.483500
## 191 3.983000 6.970000
## 192 3.983665 6.640794
## 193 4.005000 5.921000
## 194 4.080936 6.607081
## 195 4.089000 6.391000
## 196 4.159000 6.880000
## 197 4.171000 6.085000
## 198 4.257000 6.717000
## 199 4.266000 5.653000
## 200 4.308000 7.115000
## 201 4.354710 6.724125
## 202 4.359060 5.603609
## 203 4.387000 6.996000
## 204 4.398000 6.354000
## 205 4.399000 6.112000
## 206 4.418000 7.691000
## 207 4.457000 6.647000
## 208 4.463000 5.647000
## 209 4.469000 6.721000
## 210 4.585000 6.936000
## 211 4.601289 6.609096
## 212 4.601463 6.609096
## 213 4.620300 6.702820
## 214 4.643000 7.282000
## 215 4.644000 6.315000
## 216 4.644854 6.414706
## 217 4.684000 6.246000
## 218 4.726000 6.725000
## 219 4.772000 6.248000
## 220 4.844171 6.563645
## 221 4.973000 6.282000
## 222 4.978662 6.458448
## 223 5.078000 7.003000
## 224 5.237000 6.364000
## 225 5.304000 6.708000
## 226 5.956000 7.004000
## 227 5.978000 5.814000
## 228 6.156000 6.080000
## 229 6.255000 6.250000
## 230 6.312000 7.250000
## 231 6.820000 7.530000# Compare maximum fertility rates in 1960 vs. maximum fertility rates in 2021
aggregate(fertility_rates$"1960" ~ fertility_rates$"2021", FUN = max)## fertility_rates$"2021" fertility_rates$"1960"
## 1 0.772000 5.067000
## 2 0.808000 5.949000
## 3 0.907000 4.796000
## 4 1.005000 5.162000
## 5 1.088000 4.933000
## 6 1.120000 5.760000
## 7 1.140000 3.620000
## 8 1.160000 2.240000
## 9 1.164000 4.451000
## 10 1.180000 4.820000
## 11 1.190000 2.860000
## 12 1.250000 2.400000
## 13 1.300000 3.510000
## 14 1.321000 3.512000
## 15 1.330000 2.980000
## 16 1.331000 6.248000
## 17 1.340000 2.560000
## 18 1.350000 3.907000
## 19 1.352000 5.580000
## 20 1.380000 3.160000
## 21 1.383908 4.801000
## 22 1.389000 4.818000
## 23 1.390000 6.455000
## 24 1.396865 4.378614
## 25 1.399000 6.967000
## 26 1.410000 6.167000
## 27 1.413000 6.704000
## 28 1.430000 3.811000
## 29 1.442000 4.129000
## 30 1.457000 2.270000
## 31 1.460000 6.718000
## 32 1.480000 2.690000
## 33 1.483000 2.670000
## 34 1.488552 2.897990
## 35 1.489433 4.561606
## 36 1.493000 2.827000
## 37 1.498219 4.531774
## 38 1.504510 2.605864
## 39 1.516846 4.864229
## 40 1.520000 5.878000
## 41 1.520288 2.571186
## 42 1.520442 4.841982
## 43 1.522000 6.359000
## 44 1.531000 7.169000
## 45 1.533000 6.712000
## 46 1.537000 4.697000
## 47 1.544788 3.019437
## 48 1.550000 2.850000
## 49 1.550993 2.491059
## 50 1.560000 2.690000
## 51 1.564000 2.875000
## 52 1.570000 1.940000
## 53 1.575000 4.786000
## 54 1.580000 4.602000
## 55 1.581000 4.202000
## 56 1.589351 3.285589
## 57 1.590000 2.020000
## 58 1.595000 6.866000
## 59 1.600000 3.967000
## 60 1.610000 1.980000
## 61 1.620000 3.120000
## 62 1.626000 5.345000
## 63 1.633000 4.333000
## 64 1.640000 4.240000
## 65 1.640018 3.668255
## 66 1.641000 6.061000
## 67 1.664000 3.654000
## 68 1.664522 5.727913
## 69 1.669000 5.790000
## 70 1.670000 2.170000
## 71 1.692000 7.301000
## 72 1.693061 2.831429
## 73 1.699000 5.888000
## 74 1.700000 3.453000
## 75 1.717000 6.735000
## 76 1.720000 3.780000
## 77 1.750000 3.498000
## 78 1.778000 6.836000
## 79 1.797000 7.286000
## 80 1.800000 2.340000
## 81 1.801000 6.647000
## 82 1.803000 6.634000
## 83 1.806000 3.328000
## 84 1.809000 3.568000
## 85 1.811000 7.152000
## 86 1.820000 4.290000
## 87 1.822000 6.763000
## 88 1.824446 4.970095
## 89 1.830000 2.850000
## 90 1.835001 3.103683
## 91 1.848000 3.012000
## 92 1.852014 5.959449
## 93 1.853283 5.865889
## 94 1.864022 5.945441
## 95 1.883205 3.184230
## 96 1.885000 3.075000
## 97 1.889000 6.383000
## 98 1.896000 6.885000
## 99 1.944000 6.280000
## 100 1.981000 6.784000
## 101 1.990000 5.465000
## 102 2.004000 6.743000
## 103 2.010000 6.500000
## 104 2.020000 6.278000
## 105 2.026000 6.721000
## 106 2.029000 6.030000
## 107 2.031000 5.921000
## 108 2.081000 2.942000
## 109 2.086000 6.942000
## 110 2.091000 5.818000
## 111 2.110000 7.162000
## 112 2.136727 5.219097
## 113 2.151000 5.983000
## 114 2.175000 5.547000
## 115 2.192000 6.941000
## 116 2.211000 6.358000
## 117 2.240930 6.078350
## 118 2.273000 7.555000
## 119 2.273169 4.695876
## 120 2.321000 7.159000
## 121 2.325000 5.844000
## 122 2.328000 7.040000
## 123 2.344000 6.251000
## 124 2.348000 6.608000
## 125 2.350962 6.166562
## 126 2.363000 7.458000
## 127 2.374000 6.159000
## 128 2.384585 5.251170
## 129 2.393171 5.301390
## 130 2.395000 6.955000
## 131 2.397000 6.372000
## 132 2.415000 5.948000
## 133 2.427000 7.626000
## 134 2.462000 7.373000
## 135 2.469000 6.500000
## 136 2.475000 6.461000
## 137 2.496000 6.292000
## 138 2.569000 5.906000
## 139 2.578281 5.983878
## 140 2.618000 6.358000
## 141 2.623000 7.247000
## 142 2.629064 6.950247
## 143 2.655901 5.357909
## 144 2.660823 7.002555
## 145 2.667000 6.590000
## 146 2.670537 7.002555
## 147 2.711000 6.686000
## 148 2.729000 8.234000
## 149 2.747000 7.485000
## 150 2.748000 7.148000
## 151 2.791000 6.628000
## 152 2.804000 6.828000
## 153 2.814000 6.208000
## 154 2.830000 7.669000
## 155 2.837000 6.827000
## 156 2.839000 6.752000
## 157 2.859614 5.069770
## 158 2.889000 7.503000
## 159 2.890000 5.376000
## 160 2.917000 6.794000
## 161 3.000000 3.866000
## 162 3.018000 5.819000
## 163 3.142565 6.934332
## 164 3.149000 6.319000
## 165 3.163000 4.778000
## 166 3.173000 6.613000
## 167 3.186000 6.547000
## 168 3.215000 6.018000
## 169 3.237000 6.885000
## 170 3.241013 6.768236
## 171 3.303000 6.205000
## 172 3.304000 6.553000
## 173 3.320000 4.530000
## 174 3.335000 7.632000
## 175 3.470000 6.800000
## 176 3.491000 7.220000
## 177 3.496000 5.300000
## 178 3.519000 5.269000
## 179 3.563000 6.847000
## 180 3.735000 6.863000
## 181 3.795000 7.938000
## 182 3.821000 8.187000
## 183 3.823000 6.242000
## 184 3.843961 6.629390
## 185 3.851000 7.300000
## 186 3.867000 6.483000
## 187 3.917000 7.029000
## 188 3.921027 6.622652
## 189 3.930000 7.646000
## 190 3.978000 6.792000
## 191 3.983000 6.970000
## 192 3.983665 6.640794
## 193 4.005000 5.921000
## 194 4.080936 6.607081
## 195 4.089000 6.391000
## 196 4.159000 6.880000
## 197 4.171000 6.085000
## 198 4.257000 6.717000
## 199 4.266000 5.653000
## 200 4.308000 7.115000
## 201 4.354710 6.724125
## 202 4.359060 5.603609
## 203 4.387000 6.996000
## 204 4.398000 6.354000
## 205 4.399000 6.112000
## 206 4.418000 7.691000
## 207 4.457000 6.647000
## 208 4.463000 5.647000
## 209 4.469000 6.721000
## 210 4.585000 6.936000
## 211 4.601289 6.609096
## 212 4.601463 6.609096
## 213 4.620300 6.702820
## 214 4.643000 7.282000
## 215 4.644000 6.315000
## 216 4.644854 6.414706
## 217 4.684000 6.246000
## 218 4.726000 6.725000
## 219 4.772000 6.248000
## 220 4.844171 6.563645
## 221 4.973000 6.282000
## 222 4.978662 6.458448
## 223 5.078000 7.003000
## 224 5.237000 6.364000
## 225 5.304000 6.708000
## 226 5.956000 7.004000
## 227 5.978000 5.814000
## 228 6.156000 6.080000
## 229 6.255000 6.250000
## 230 6.312000 7.250000
## 231 6.820000 7.530000# Compare minimum fertility rates in 1960 vs. minimum fertility rates in 2021
aggregate(fertility_rates$"1960" ~ fertility_rates$"2021", FUN = min)## fertility_rates$"2021" fertility_rates$"1960"
## 1 0.772000 5.067000
## 2 0.808000 5.949000
## 3 0.907000 4.796000
## 4 1.005000 5.162000
## 5 1.088000 4.933000
## 6 1.120000 5.760000
## 7 1.140000 3.620000
## 8 1.160000 2.240000
## 9 1.164000 4.451000
## 10 1.180000 4.820000
## 11 1.190000 2.860000
## 12 1.250000 2.400000
## 13 1.300000 2.000000
## 14 1.321000 3.512000
## 15 1.330000 2.980000
## 16 1.331000 6.248000
## 17 1.340000 2.560000
## 18 1.350000 3.907000
## 19 1.352000 5.580000
## 20 1.380000 2.290000
## 21 1.383908 4.801000
## 22 1.389000 4.818000
## 23 1.390000 2.230000
## 24 1.396865 4.378614
## 25 1.399000 6.967000
## 26 1.410000 6.167000
## 27 1.413000 6.704000
## 28 1.430000 3.811000
## 29 1.442000 4.129000
## 30 1.457000 2.270000
## 31 1.460000 2.720000
## 32 1.480000 2.113000
## 33 1.483000 2.670000
## 34 1.488552 2.897990
## 35 1.489433 4.561606
## 36 1.493000 2.520000
## 37 1.498219 4.531774
## 38 1.504510 2.605864
## 39 1.516846 4.864229
## 40 1.520000 2.440000
## 41 1.520288 2.571186
## 42 1.520442 4.841982
## 43 1.522000 6.359000
## 44 1.531000 7.169000
## 45 1.533000 6.712000
## 46 1.537000 4.697000
## 47 1.544788 3.019437
## 48 1.550000 2.850000
## 49 1.550993 2.491059
## 50 1.560000 2.690000
## 51 1.564000 2.875000
## 52 1.570000 1.940000
## 53 1.575000 4.786000
## 54 1.580000 2.310000
## 55 1.581000 4.202000
## 56 1.589351 3.285589
## 57 1.590000 2.020000
## 58 1.595000 6.866000
## 59 1.600000 2.540000
## 60 1.610000 1.980000
## 61 1.620000 2.232000
## 62 1.626000 5.345000
## 63 1.633000 4.333000
## 64 1.640000 2.185000
## 65 1.640018 3.668255
## 66 1.641000 6.061000
## 67 1.664000 3.654000
## 68 1.664522 5.727913
## 69 1.669000 5.790000
## 70 1.670000 2.170000
## 71 1.692000 6.804000
## 72 1.693061 2.831429
## 73 1.699000 5.888000
## 74 1.700000 3.453000
## 75 1.717000 6.735000
## 76 1.720000 2.570000
## 77 1.750000 3.498000
## 78 1.778000 6.836000
## 79 1.797000 7.286000
## 80 1.800000 2.340000
## 81 1.801000 6.647000
## 82 1.803000 6.407000
## 83 1.806000 3.328000
## 84 1.809000 3.568000
## 85 1.811000 7.152000
## 86 1.820000 4.290000
## 87 1.822000 6.763000
## 88 1.824446 4.970095
## 89 1.830000 2.090000
## 90 1.835001 3.103683
## 91 1.848000 3.012000
## 92 1.852014 5.959449
## 93 1.853283 5.865889
## 94 1.864022 5.945441
## 95 1.883205 3.184230
## 96 1.885000 3.075000
## 97 1.889000 6.383000
## 98 1.896000 6.885000
## 99 1.944000 6.280000
## 100 1.981000 6.784000
## 101 1.990000 5.465000
## 102 2.004000 6.743000
## 103 2.010000 5.445000
## 104 2.020000 6.278000
## 105 2.026000 6.721000
## 106 2.029000 6.030000
## 107 2.031000 5.921000
## 108 2.081000 2.942000
## 109 2.086000 6.942000
## 110 2.091000 5.818000
## 111 2.110000 7.162000
## 112 2.136727 5.219097
## 113 2.151000 5.983000
## 114 2.175000 5.547000
## 115 2.192000 6.941000
## 116 2.211000 6.358000
## 117 2.240930 6.078350
## 118 2.273000 7.555000
## 119 2.273169 4.695876
## 120 2.321000 7.159000
## 121 2.325000 5.844000
## 122 2.328000 7.040000
## 123 2.344000 6.251000
## 124 2.348000 6.608000
## 125 2.350962 6.166562
## 126 2.363000 7.458000
## 127 2.374000 6.159000
## 128 2.384585 5.251170
## 129 2.393171 5.301390
## 130 2.395000 6.955000
## 131 2.397000 6.372000
## 132 2.415000 5.948000
## 133 2.427000 7.626000
## 134 2.462000 7.373000
## 135 2.469000 6.500000
## 136 2.475000 6.461000
## 137 2.496000 6.292000
## 138 2.569000 5.906000
## 139 2.578281 5.983878
## 140 2.618000 6.358000
## 141 2.623000 7.247000
## 142 2.629064 6.950247
## 143 2.655901 5.357909
## 144 2.660823 7.002555
## 145 2.667000 6.590000
## 146 2.670537 7.002555
## 147 2.711000 6.686000
## 148 2.729000 8.234000
## 149 2.747000 7.485000
## 150 2.748000 7.148000
## 151 2.791000 6.628000
## 152 2.804000 6.828000
## 153 2.814000 6.208000
## 154 2.830000 7.669000
## 155 2.837000 6.827000
## 156 2.839000 6.752000
## 157 2.859614 5.069770
## 158 2.889000 7.503000
## 159 2.890000 5.376000
## 160 2.917000 6.794000
## 161 3.000000 3.866000
## 162 3.018000 5.819000
## 163 3.142565 6.934332
## 164 3.149000 6.319000
## 165 3.163000 4.778000
## 166 3.173000 6.613000
## 167 3.186000 6.547000
## 168 3.215000 6.018000
## 169 3.237000 6.885000
## 170 3.241013 6.768236
## 171 3.303000 6.205000
## 172 3.304000 6.553000
## 173 3.320000 4.530000
## 174 3.335000 7.632000
## 175 3.470000 6.800000
## 176 3.491000 4.422000
## 177 3.496000 5.300000
## 178 3.519000 5.269000
## 179 3.563000 6.847000
## 180 3.735000 6.863000
## 181 3.795000 7.938000
## 182 3.821000 8.187000
## 183 3.823000 6.242000
## 184 3.843961 6.629390
## 185 3.851000 7.300000
## 186 3.867000 6.483000
## 187 3.917000 7.029000
## 188 3.921027 6.622652
## 189 3.930000 7.646000
## 190 3.978000 6.175000
## 191 3.983000 6.970000
## 192 3.983665 6.640794
## 193 4.005000 5.921000
## 194 4.080936 6.607081
## 195 4.089000 6.391000
## 196 4.159000 6.880000
## 197 4.171000 6.085000
## 198 4.257000 6.717000
## 199 4.266000 5.653000
## 200 4.308000 7.115000
## 201 4.354710 6.724125
## 202 4.359060 5.603609
## 203 4.387000 6.996000
## 204 4.398000 6.354000
## 205 4.399000 6.112000
## 206 4.418000 7.691000
## 207 4.457000 6.647000
## 208 4.463000 5.647000
## 209 4.469000 6.721000
## 210 4.585000 6.936000
## 211 4.601289 6.609096
## 212 4.601463 6.609096
## 213 4.620300 6.702820
## 214 4.643000 7.282000
## 215 4.644000 6.315000
## 216 4.644854 6.414706
## 217 4.684000 6.246000
## 218 4.726000 6.725000
## 219 4.772000 6.248000
## 220 4.844171 6.563645
## 221 4.973000 6.282000
## 222 4.978662 6.458448
## 223 5.078000 7.003000
## 224 5.237000 6.364000
## 225 5.304000 6.708000
## 226 5.956000 7.004000
## 227 5.978000 5.814000
## 228 6.156000 6.080000
## 229 6.255000 6.250000
## 230 6.312000 7.250000
## 231 6.820000 7.530000# Find Average Total Fertility rate by Country Name in 1960 vs. 2021.
# Assign the result to avg_fertility_rate.
avg_fertility_rate <- aggregate(fertility_rates$"1960"
~ fertility_rates$"2021"
+ fertility_rates$"Country Name",
FUN = mean)Step 5: Create figures
Average Total Fertility Rate by Country (1960 vs. 2021)
# Create a scatter plot of the data to find general pattern of fertility rates
ggplot(avg_fertility_rate, aes(x = `fertility_rates$"2021"`, y = `fertility_rates$"1960"`)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
xlab("Average Total Fertility Rate in 2021") +
ylab("Average Total Fertility Rate in 1960") +
ggtitle("Average Total Fertility Rate by Country (1960 vs. 2021)")## `geom_smooth()` using formula = 'y ~ x'
Average Total Fertility Rate in European Countries (1960-2021)
# Find unique country names in fertility_rates table
country_names <- unique(fertility_rates$`Country Name`)
sorted_country_names <- sort(country_names)
sorted_country_names## [1] "Afghanistan"
## [2] "Africa Eastern and Southern"
## [3] "Africa Western and Central"
## [4] "Albania"
## [5] "Algeria"
## [6] "American Samoa"
## [7] "Andorra"
## [8] "Angola"
## [9] "Antigua and Barbuda"
## [10] "Arab World"
## [11] "Argentina"
## [12] "Armenia"
## [13] "Aruba"
## [14] "Australia"
## [15] "Austria"
## [16] "Azerbaijan"
## [17] "Bahamas, The"
## [18] "Bahrain"
## [19] "Bangladesh"
## [20] "Barbados"
## [21] "Belarus"
## [22] "Belgium"
## [23] "Belize"
## [24] "Benin"
## [25] "Bermuda"
## [26] "Bhutan"
## [27] "Bolivia"
## [28] "Bosnia and Herzegovina"
## [29] "Botswana"
## [30] "Brazil"
## [31] "British Virgin Islands"
## [32] "Brunei Darussalam"
## [33] "Bulgaria"
## [34] "Burkina Faso"
## [35] "Burundi"
## [36] "Cabo Verde"
## [37] "Cambodia"
## [38] "Cameroon"
## [39] "Canada"
## [40] "Caribbean small states"
## [41] "Cayman Islands"
## [42] "Central African Republic"
## [43] "Central Europe and the Baltics"
## [44] "Chad"
## [45] "Channel Islands"
## [46] "Chile"
## [47] "China"
## [48] "Colombia"
## [49] "Comoros"
## [50] "Congo, Dem. Rep."
## [51] "Congo, Rep."
## [52] "Costa Rica"
## [53] "Cote d'Ivoire"
## [54] "Croatia"
## [55] "Cuba"
## [56] "Curacao"
## [57] "Cyprus"
## [58] "Czechia"
## [59] "Denmark"
## [60] "Djibouti"
## [61] "Dominica"
## [62] "Dominican Republic"
## [63] "Early-demographic dividend"
## [64] "East Asia & Pacific"
## [65] "East Asia & Pacific (excluding high income)"
## [66] "East Asia & Pacific (IDA & IBRD countries)"
## [67] "Ecuador"
## [68] "Egypt, Arab Rep."
## [69] "El Salvador"
## [70] "Equatorial Guinea"
## [71] "Eritrea"
## [72] "Estonia"
## [73] "Eswatini"
## [74] "Ethiopia"
## [75] "Euro area"
## [76] "Europe & Central Asia"
## [77] "Europe & Central Asia (excluding high income)"
## [78] "Europe & Central Asia (IDA & IBRD countries)"
## [79] "European Union"
## [80] "Faroe Islands"
## [81] "Fiji"
## [82] "Finland"
## [83] "Fragile and conflict affected situations"
## [84] "France"
## [85] "French Polynesia"
## [86] "Gabon"
## [87] "Gambia, The"
## [88] "Georgia"
## [89] "Germany"
## [90] "Ghana"
## [91] "Gibraltar"
## [92] "Greece"
## [93] "Greenland"
## [94] "Grenada"
## [95] "Guam"
## [96] "Guatemala"
## [97] "Guinea"
## [98] "Guinea-Bissau"
## [99] "Guyana"
## [100] "Haiti"
## [101] "Heavily indebted poor countries (HIPC)"
## [102] "High income"
## [103] "Honduras"
## [104] "Hong Kong SAR, China"
## [105] "Hungary"
## [106] "IBRD only"
## [107] "Iceland"
## [108] "IDA & IBRD total"
## [109] "IDA blend"
## [110] "IDA only"
## [111] "IDA total"
## [112] "India"
## [113] "Indonesia"
## [114] "Iran, Islamic Rep."
## [115] "Iraq"
## [116] "Ireland"
## [117] "Isle of Man"
## [118] "Israel"
## [119] "Italy"
## [120] "Jamaica"
## [121] "Japan"
## [122] "Jordan"
## [123] "Kazakhstan"
## [124] "Kenya"
## [125] "Kiribati"
## [126] "Korea, Dem. People's Rep."
## [127] "Korea, Rep."
## [128] "Kosovo"
## [129] "Kuwait"
## [130] "Kyrgyz Republic"
## [131] "Lao PDR"
## [132] "Late-demographic dividend"
## [133] "Latin America & Caribbean"
## [134] "Latin America & Caribbean (excluding high income)"
## [135] "Latin America & the Caribbean (IDA & IBRD countries)"
## [136] "Latvia"
## [137] "Least developed countries: UN classification"
## [138] "Lebanon"
## [139] "Lesotho"
## [140] "Liberia"
## [141] "Libya"
## [142] "Liechtenstein"
## [143] "Lithuania"
## [144] "Low & middle income"
## [145] "Low income"
## [146] "Lower middle income"
## [147] "Luxembourg"
## [148] "Macao SAR, China"
## [149] "Madagascar"
## [150] "Malawi"
## [151] "Malaysia"
## [152] "Maldives"
## [153] "Mali"
## [154] "Malta"
## [155] "Marshall Islands"
## [156] "Mauritania"
## [157] "Mauritius"
## [158] "Mexico"
## [159] "Micronesia, Fed. Sts."
## [160] "Middle East & North Africa"
## [161] "Middle East & North Africa (excluding high income)"
## [162] "Middle East & North Africa (IDA & IBRD countries)"
## [163] "Middle income"
## [164] "Moldova"
## [165] "Monaco"
## [166] "Mongolia"
## [167] "Montenegro"
## [168] "Morocco"
## [169] "Mozambique"
## [170] "Myanmar"
## [171] "Namibia"
## [172] "Nauru"
## [173] "Nepal"
## [174] "Netherlands"
## [175] "New Caledonia"
## [176] "New Zealand"
## [177] "Nicaragua"
## [178] "Niger"
## [179] "Nigeria"
## [180] "North America"
## [181] "North Macedonia"
## [182] "Northern Mariana Islands"
## [183] "Norway"
## [184] "Not classified"
## [185] "OECD members"
## [186] "Oman"
## [187] "Other small states"
## [188] "Pacific island small states"
## [189] "Pakistan"
## [190] "Palau"
## [191] "Panama"
## [192] "Papua New Guinea"
## [193] "Paraguay"
## [194] "Peru"
## [195] "Philippines"
## [196] "Poland"
## [197] "Portugal"
## [198] "Post-demographic dividend"
## [199] "Pre-demographic dividend"
## [200] "Puerto Rico"
## [201] "Qatar"
## [202] "Romania"
## [203] "Russian Federation"
## [204] "Rwanda"
## [205] "Samoa"
## [206] "San Marino"
## [207] "Sao Tome and Principe"
## [208] "Saudi Arabia"
## [209] "Senegal"
## [210] "Serbia"
## [211] "Seychelles"
## [212] "Sierra Leone"
## [213] "Singapore"
## [214] "Sint Maarten (Dutch part)"
## [215] "Slovak Republic"
## [216] "Slovenia"
## [217] "Small states"
## [218] "Solomon Islands"
## [219] "Somalia"
## [220] "South Africa"
## [221] "South Asia"
## [222] "South Asia (IDA & IBRD)"
## [223] "South Sudan"
## [224] "Spain"
## [225] "Sri Lanka"
## [226] "St. Kitts and Nevis"
## [227] "St. Lucia"
## [228] "St. Martin (French part)"
## [229] "St. Vincent and the Grenadines"
## [230] "Sub-Saharan Africa"
## [231] "Sub-Saharan Africa (excluding high income)"
## [232] "Sub-Saharan Africa (IDA & IBRD countries)"
## [233] "Sudan"
## [234] "Suriname"
## [235] "Sweden"
## [236] "Switzerland"
## [237] "Syrian Arab Republic"
## [238] "Tajikistan"
## [239] "Tanzania"
## [240] "Thailand"
## [241] "Timor-Leste"
## [242] "Togo"
## [243] "Tonga"
## [244] "Trinidad and Tobago"
## [245] "Tunisia"
## [246] "Turkiye"
## [247] "Turkmenistan"
## [248] "Turks and Caicos Islands"
## [249] "Tuvalu"
## [250] "Uganda"
## [251] "Ukraine"
## [252] "United Arab Emirates"
## [253] "United Kingdom"
## [254] "United States"
## [255] "Upper middle income"
## [256] "Uruguay"
## [257] "Uzbekistan"
## [258] "Vanuatu"
## [259] "Venezuela, RB"
## [260] "Vietnam"
## [261] "Virgin Islands (U.S.)"
## [262] "West Bank and Gaza"
## [263] "World"
## [264] "Yemen, Rep."
## [265] "Zambia"
## [266] "Zimbabwe"# Filter the dataset to include only European countries
european_countries <- c("Albania", "Andorra", "Austria", "Belarus", "Belgium",
"Bosnia and Herzegovina", "Bulgaria", "Channel Islands",
"Croatia", "Cyprus", "Czechia", "Denmark", "Estonia",
"Faroe Islands", "Finland", "France", "Germany",
"Gibraltar", "Greece", "Hungary", "Iceland", "Ireland",
"Isle of Man", "Italy", "Latvia", "Liechtenstein",
"Lithuania", "Luxembourg", "Malta", "Moldova",
"Monaco", "Montenegro", "Netherlands",
"North Macedonia", "Norway", "Poland",
"Portugal", "Romania", "Russian Federation",
"San Marino", "Serbia","Slovakia","Slovenia","Spain",
"Sweden","Switzerland","Ukraine","United Kingdom",
"Vatican City")
# Replace with the list of European countries
fertility_rates_europe <- fertility_rates %>%
filter(`Country Name` %in% european_countries)
# Reshape the data from wide to long format
fertility_rates_long <- fertility_rates_europe %>%
pivot_longer(cols = `1960`:`2021`,
names_to = "Year",
values_to = "Fertility_Rate")
# Create the plot with custom x-axis labels
p <- ggplot(fertility_rates_long,
aes(x = Year,
y = Fertility_Rate,
color = `Country Name`,
group = `Country Name`)) +
geom_line() +
xlab("Year") +
ylab("Fertility Rate") +
ggtitle("Average Total Fertility Rate in European Countries (1960-2021)") +
scale_x_discrete(breaks = seq(1960, 2021, by = 5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p 
# Save the plot to a file with a resolution of 400 dpi
ggsave("Documents/data_analytics/fertility_rate_case_study/fertility_rates_europe.png",
plot = p,
width = 10,
dpi = 1000)## Saving 10 x 5 in imageAverage Total Fertility Rate in African Countries (1960-2021)
# Filter the dataset to include only African countries
african_countries <- c("Angola", "Benin", "Burkina Faso", "Burundi", "Cabo Verde",
"Cameroon", "Central African Republic", "Chad", "Comoros",
"Congo, Dem. Rep.", "Congo, Rep.", "Cote d'Ivoire",
"Djibouti", "Egypt, Arab Rep.", "Equatorial Guinea",
"Eritrea", "Eswatini", "Ethiopia", "Gabon", "Gambia, The",
"Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho",
"Liberia", "Libya", "Madagascar", "Malawi", "Mali",
"Mauritania", "Mauritius", "Morocco", "Mozambique",
"Namibia", "Niger", "Nigeria", "Rwanda",
"Sao Tome and Principe", "Senegal",
"Seychelles","Sierra Leone","Somalia","South Africa",
"South Sudan","Sudan","Tanzania","Togo","Tunisia",
"Uganda","Zambia","Zimbabwe")
# Replace with the list of African countries
fertility_rates_africa <- fertility_rates %>%
filter(`Country Name` %in% african_countries)
# Reshape the data from wide to long format
fertility_rates_long <- fertility_rates_africa %>%
pivot_longer(cols = `1960`:`2021`,
names_to = "Year",
values_to = "Fertility_Rate")
# Create the plot with custom x-axis labels
p <- ggplot(fertility_rates_long,
aes(x = Year,
y = Fertility_Rate,
color = `Country Name`,
group = `Country Name`)) +
geom_line() +
xlab("Year") +
ylab("Fertility Rate") +
ggtitle("Average Total Fertility Rate in African Countries (1960-2021)") +
scale_x_discrete(breaks = seq(1960, 2021, by = 5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p 
# Save the plot to a file with a resolution of 400 dpi
ggsave("Documents/data_analytics/fertility_rate_case_study/fertility_rates_africa.png",
plot = p,
width = 10,
dpi = 1000)## Saving 10 x 5 in imageAverage Total Fertility Rate in Asian Countries (1960-2021)
# Filter the dataset to include only Asian countries
asian_countries <- c("Afghanistan", "Armenia", "Azerbaijan", "Bahrain", "Bangladesh",
"Bhutan", "Brunei Darussalam", "Cambodia", "China", "Georgia",
"Hong Kong SAR, China", "India", "Indonesia", "Iran, Islamic Rep.",
"Iraq", "Israel", "Japan", "Jordan", "Kazakhstan",
"Korea, Dem. People's Rep.", "Korea, Rep.", "Kuwait",
"Kyrgyz Republic", "Lao PDR", "Lebanon",
"Macao SAR, China", "Malaysia", "Maldives",
"Mongolia", "Myanmar", "Nepal",
"Oman", "Pakistan","Palestine","Philippines","Qatar",
"Russian Federation","Saudi Arabia","Singapore","Sri Lanka",
"Syrian Arab Republic","Taiwan, China","Tajikistan","Thailand",
"Timor-Leste","Turkey","Turkmenistan","United Arab Emirates",
"Uzbekistan","Vietnam","Yemen, Rep.")
# Replace with the list of Asian countries
fertility_rates_asia <- fertility_rates %>%
filter(`Country Name` %in% asian_countries)
# Reshape the data from wide to long format
fertility_rates_long <- fertility_rates_asia %>%
pivot_longer(cols = `1960`:`2021`,
names_to = "Year",
values_to = "Fertility_Rate")
# Create the plot with custom x-axis labels
p <- ggplot(fertility_rates_long,
aes(x = Year,
y = Fertility_Rate,
color = `Country Name`,
group = `Country Name`)) +
geom_line() +
xlab("Year") +
ylab("Fertility Rate") +
ggtitle("Average Total Fertility Rate in Asian Countries (1960-2021)") +
scale_x_discrete(breaks = seq(1960, 2021, by = 5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p 
# Save the plot to a file with a resolution of 400 dpi
ggsave("Documents/data_analytics/fertility_rate_case_study/fertility_rates_asia.png",
plot = p,
width = 10,
dpi = 1000)## Saving 10 x 5 in imageAverage Total Fertility Rate in North American Countries (1960-2021)
# Filter the dataset to include only North American countries
north_american_countries <- c("Antigua and Barbuda", "Bahamas, The", "Barbados",
"Belize", "Canada", "Costa Rica", "Cuba", "Dominica",
"Dominican Republic", "El Salvador", "Greenland",
"Grenada", "Guatemala", "Haiti", "Honduras",
"Jamaica", "Mexico", "Nicaragua", "Panama",
"St. Kitts and Nevis", "St. Lucia",
"St. Vincent and the Grenadines",
"Trinidad and Tobago", "United States")
# Replace with the list of North American countries
fertility_rates_north_america <- fertility_rates %>%
filter(`Country Name` %in% north_american_countries)
# Reshape the data from wide to long format
fertility_rates_long <- fertility_rates_north_america %>%
pivot_longer(cols = `1960`:`2021`,
names_to = "Year",
values_to = "Fertility_Rate")
# Create the plot with custom x-axis labels
p <- ggplot(fertility_rates_long,
aes(x = Year,
y = Fertility_Rate,
color = `Country Name`,
group = `Country Name`)) +
geom_line() +
xlab("Year") +
ylab("Fertility Rate") +
ggtitle("Average Total Fertility Rate in North American Countries (1960-2021)") +
scale_x_discrete(breaks = seq(1960, 2021, by = 5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p 
# Save the plot to a file with a resolution of 400 dpi
ggsave("Documents/data_analytics/fertility_rate_case_study/fertility_rates_north_america.png",
plot = p,
width = 10,
dpi = 1000)## Saving 10 x 5 in imageAverage Total Fertility Rate in South American Countries (1960-2021)
# Filter the dataset to include only South American countries
south_american_countries <- c("Argentina", "Bolivia", "Brazil", "Chile",
"Colombia", "Ecuador", "Guyana", "Paraguay",
"Peru", "Suriname", "Uruguay", "Venezuela")
# Replace with the list of South American countries
fertility_rates_south_america <- fertility_rates %>%
filter(`Country Name` %in% south_american_countries)
# Reshape the data from wide to long format
fertility_rates_long <- fertility_rates_south_america %>%
pivot_longer(cols = `1960`:`2021`,
names_to = "Year",
values_to = "Fertility_Rate")
# Create the plot with custom x-axis labels
p <- ggplot(fertility_rates_long,
aes(x = Year,
y = Fertility_Rate,
color = `Country Name`,
group = `Country Name`)) +
geom_line() +
xlab("Year") +
ylab("Fertility Rate") +
ggtitle("Average Total Fertility Rate in South American Countries (1960-2021)") +
scale_x_discrete(breaks = seq(1960, 2021, by = 5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p 
# Save the plot to a file with a resolution of 400 dpi
ggsave("Documents/data_analytics/fertility_rate_case_study/fertility_rates_south_america.png",
plot = p,
width = 10,
dpi = 1000)## Saving 10 x 5 in imageAverage Total Fertility Rate in Oceanic Countries (1960-2021)
# Filter the dataset to include only Oceanic countries
oceanic_countries <- c("Australia", "Fiji", "Kiribati", "Marshall Islands",
"Micronesia, Fed. Sts.", "Nauru", "New Zealand", "Palau",
"Papua New Guinea", "Samoa", "Solomon Islands", "Tonga",
"Tuvalu", "Vanuatu")
# Replace with the list of Oceanic countries
fertility_rates_oceania <- fertility_rates %>%
filter(`Country Name` %in% oceanic_countries)
# Reshape the data from wide to long format
fertility_rates_long <- fertility_rates_oceania %>%
pivot_longer(cols = `1960`:`2021`,
names_to = "Year",
values_to = "Fertility_Rate")
# Create the plot with custom x-axis labels
p <- ggplot(fertility_rates_long,
aes(x = Year,
y = Fertility_Rate,
color = `Country Name`,
group = `Country Name`)) +
geom_line() +
xlab("Year") +
ylab("Fertility Rate") +
ggtitle("Average Total Fertility Rate in Oceanic Countries (1960-2021)") +
scale_x_discrete(breaks = seq(1960, 2021, by = 5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
p 
# Save the plot to a file with a resolution of 400 dpi
ggsave("Documents/data_analytics/fertility_rate_case_study/fertility_rates_oceania.png",
plot = p,
width = 10,
dpi = 1000)## Saving 10 x 5 in imageAverage Total Fertility Rate by Geographic Region (1960-2021)
# Combine the data for all regions into one data frame
fertility_rates_all_regions <- bind_rows(
fertility_rates_africa %>% mutate(Region = "Africa"),
fertility_rates_asia %>% mutate(Region = "Asia"),
fertility_rates_europe %>% mutate(Region = "Europe"),
fertility_rates_north_america %>% mutate(Region = "North America"),
fertility_rates_oceania %>% mutate(Region = "Oceania"),
fertility_rates_south_america %>% mutate(Region = "South America")
)
# Reshape the data from wide to long format
fertility_rates_long <- fertility_rates_all_regions %>%
pivot_longer(cols = `1960`:`2021`,
names_to = "Year",
values_to = "Fertility_Rate")
# Create the plot with custom x-axis labels and facets
p1 <- ggplot(fertility_rates_long,
aes(x = Year,
y = Fertility_Rate,
color = `Country Name`,
group = `Country Name`)) +
geom_line() +
xlab("Year") +
ylab("Fertility Rate") +
ggtitle("Average Total Fertility Rate by Region (1960-2021)") +
scale_x_discrete(breaks = seq(1960, 2021, by = 5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none") +
facet_wrap(~ Region)
p1
# Save the plot with the graphs to a file with a resolution of 400 dpi
ggsave("Documents/data_analytics/fertility_rate_case_study/fertility_rates_all_regions_graphs.png",
plot = p1,
width = 10,
dpi = 1000)## Saving 10 x 5 in image# Create a separate plot for the legendStep 6: Export data, create Tableau dashboard
# Overall, it seems that many countries are experiencing a pronounced downward
# trend in terms of average total fertility rate. To make this more clear,
# let's export fertility_rates_long.csv to Tableau to create an interactive
# dashboard.
write.csv(fertility_rates_long,
"Documents/data_analytics/fertility_rate_case_study/fertility_rates_all_regions.csv",
row.names = FALSE)Step 7: Explore Tableau dashboard
- Click here to see the corresponding Tableau dashboard.